会议论文

Lin, QH

英文

2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN

2161-4393

2023

International Joint Conference on Neural Networks (IJCNN)

IEEE International Joint Conference on Neural Networks (IJCNN)

JUN 18-23, 2023

Broadbeach, AUSTRALIA

[Zhang, Chao-Ying;Li, Wei-Xing;Lin, Qiu-Hua] Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China.^[Kuang, Li-Dan] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-6654-8867-9

10.1109/IJCNN54540.2023.10191199

National Natural Science Foundation of China [61871067, 61901061]; Fundamental Research Funds for the Central Universities, China [DUT20ZD220]; Supercomputing Center of Dalian University of Technology

本校为其他机构

Independence and sparsity are proved to be two basic features for spatial activations of functional magnetic resonance imaging (fMRI) data, and have shown efficiency in analysis of magnitude-only fMRI data. Since complex-valued fMRI data contains additional brain activity information beyond magnitude-only fMRI data, we propose to incorporate sparsity constraint into complex independent vector analysis (IVA) to take advantages of the two features in analyzing multi-subject complexvalued fMRI data. Specifically, we propose to improve a complexvalued IVA algorithm named AFIVA (adaptive fixed-poin...